An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies or...

BACKGROUND Students' attitudes toward epilepsy have been studied in several countries, but none of the studies used a quantitative scale. We aimed to determine the validity and reliability of the Public Attitudes Toward Epilepsy (PATE) scale in a homogenous population consisting of secondary and tertiary students in Malaysia and to quantify their attitudes toward epilepsy, using a web-based sur...

In this paper human normal and epileptic Electroencephalogram signals are analyzed with popular and efficient signal processing techniques like Fourier and Wavelet transform. The delta, theta, alpha, beta and gamma sub bands of EEG are obtained and studied for detection of seizure and epilepsy. The extracted feature is then applied to ANN for classification of the EEG signals. Keywords—EEG, Epi...

PURPOSE Optimized therapy in epilepsy should include individual care for cognitive functions. Here we introduce a computerized screening instrument, called "Computerized Cognitive Testing in Epilepsy" (CCTE), which allows for time-efficient repetitive assessment of the patient's cognitive profile regarding the domains of memory and attention, which are frequently impaired due to side effects of...

Ictal intracranial EEG correlates of cortical FDG and FMZ PET abnormalities in patients with neocortical epilepsy…………………. 21 4.1.1. FMZ PET is more sensitive than FDG PET in detection of seizure onset defined by intracranial EEG monitoring……21 4.1.2. Seizure onset occurs most often at the border of cortex with glucose hypometabolism……………………………….. 21 4.2. Electroclinical correlates of FDG and FM...

Interictal spikes and sharp waves in human EEG are characteristic signatures of epilepsy. These potentials originate as a result of synchronous pathological discharge of many neurons. The reliable detection of such potentials has been the long standing problem in EEG analysis, especially after long-term monitoring became common in investigation of epileptic patients. The traditional definition ...

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature co...

Epilepsy is one of the most common neurological diseases and the most common neurological chronic disease in childhood. Electroencephalography (EEG) still remains one of the most useful and effective tools in understanding and treatment of epilepsy. To this end, many computational methods have been developed for both the detection and prediction of epileptic seizures. Techniques derived from li...

Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required t...

Epilepsy is a severe neurological disorder characterized by recurrent seizures, which increases the risk of death three times more than normal. Currently Electroencephalography (EEG) has emerged as highly promising technique for diagnosis epilepsy. The majority current EEG-based epilepsy detection research have employed variety deep learning-based models, but most approaches suffer from poor ge...